Automated assistance for generating relevant and valuable search results for an entity of interest

ABSTRACT

Systems and methods are provided for identifying relevant information for an entity, referred to as a seed entity. A plurality of search queries can be generated each comprising a property of a seed entity or one of the entities associated with the seed entity (seed-linked entities). Preferably, a collection of search queries includes ones representing different properties of the seed entity and properties of different seed-linked entities. Optionally, the collection of search queries is optimized to reduce search burden. Searches can then be conducted with the search queries in one or more data sources to obtain a plurality of search results, wherein each search result comprises a hit entity and one or more entities associated with the hit entity (hit-linked entity). For each of the search results, a score can be determined taking as input (a) likelihood of match between the seed entity and the hit entity or between a seed-linked entity and a hit-linked entity, (b) presence of a new entity in the search result not present in the search queries or a difference between the new entity and an entity present in the search queries, and (c) characteristic of the new entity in the search result. Based on the scores, high priority search results can be presented a user for further analysis.

BACKGROUND

Searches, such as internet searches, are typically conducted to identifyinformation related to an entity that is not yet known to the searcherso as to provide the searcher enriched knowledge about the entity. Thesearch results may include one or more hits that are “obvious hits”. Forexample, when the entity is a person of interest and a hit includes thefully spelled name and correct social security number and birth date forthe person, such a hit can be considered an obvious hit.

Obvious hits may not be sufficient in all situations, however, as thenumber of the obvious hits from a search may be limited, and perhaps allthe obvious hits collectively may not reveal all desired informationabout the entity. This is particularly true when the entity, such as aperson, intentionally hides its identity by using false or incompleteidentification information. In such a case, a comprehensive searchstrategy is needed, which requires the intervention by a human, such asanalyst. In particular, the analyst may screen the raw search results inorder to identify potential matches. However, analysts frequently do notpossess advanced search techniques or are not readily able to use searchtools that enable them to conduct a comprehensive search.

SUMMARY

Under some approaches, a system with search functionality may be used byanalysts to discover, filter and aggregate data on an entity ofinterest. An analyst may search one or more data sources to discoverinformation about the entity of interest and manually collate andaggregate the discovered data. Such techniques may require significantexperience and skill on the part of the analyst to search one or moredata sources and draw connections between the discovered information.Further, for entities with dispersed information, a comprehensive searchmay require a wide variety of search queries applied to the datasources. In such a scenario, even an experienced analyst may struggle indesigning the search and analyze the information discovered from thesearch. These and other drawbacks exist with conventional analyst drivendata aggregation techniques.

A claimed solution rooted in computer technology overcomes problemsspecifically arising in the realm of computer technology. In variousimplementations, a computing system is configured to provide methodsthat may search one or more data sources for information on an entity ofinterest. The system may facilitate the filtering and structuring of thediscovered information either automatically or with the assistance of ananalyst. The system may further leverage the discovered information toautomatically generate and conduct additional searches of the multipledata sources to generate aggregated data on the subject of interest.

These and other features of the systems, methods, and non-transitorycomputer readable media disclosed herein, as well as the methods ofoperation and functions of the related elements of structure and thecombination of parts and economies of manufacture, will become moreapparent upon consideration of the following description and theappended claims with reference to the accompanying drawings, all ofwhich form a part of this specification, wherein like reference numeralsdesignate corresponding parts in the various figures. It is to beexpressly understood, however, that the drawings are for purposes ofillustration and description only and are not intended as a definitionof the limits of the invention.

BRIEF DESCRIPTION OF THE DRAWINGS

Certain features of various embodiments of the present technology areset forth with particularity in the appended claims. A betterunderstanding of the features and advantages of the technology will beobtained by reference to the following detailed description that setsforth illustrative embodiments, in which the principles of the inventionare utilized, and the accompanying drawings of which:

FIG. 1 illustrates the compositions of a seed cluster used for searches,and hit clusters returned from the searches. The hard links and softlinks between entities within or between the clusters are alsoindicated.

FIG. 2-4 illustrate graphic user interfaces to assist a user to conductsearches and explore search results. The user interfaces can also allowa user to receive alerts of newly obtained search results.

FIG. 5 illustrates a flowchart of an example method for identifyingrelevant information for an entity.

The figures depict various embodiments of the disclosed technology forpurposes of illustration only, wherein the figures use like referencenumerals to identify like elements. One skilled in the art will readilyrecognize from the following discussion that alternative embodiments ofthe structures and methods illustrated in the figures can be employedwithout departing from the principles of the disclosed technologydescribed herein.

DETAILED DESCRIPTION

Useful information from a data source that may be related to an entitysometimes is associated with incomplete identifying information or isnot directly linked to the entity. It is also common that theinformation is scattered in different data entries in data source or indifferent sources. For instance, a series of financial transactionsoriginated from a sender, through one or more intermediate receivers andsenders and banks, may be used to effect transfer of an amount of moneyfrom the sender to an ultimate receiver. Each transaction may berecorded in a different data source, and the individuals associated withthe transaction may use incomplete or false identifying information.This would present a great challenge for uncovering the entiretransaction.

A claimed solution rooted in computer technology overcomes problemsspecifically arising in the realm of computer technology. In variousimplementations, a method entails receiving a search query related to anentity, such as an individual or institution. Optionally, a pre-searchcan be conducted to identify useful information related to the entity inorder to construct effective search queries. For instance, thepre-search can be conducted with limited information, such as a name anddate of birth of a person. Such a simple search may reveal additionalproperties of the entity, such as social security number, city of birth,address, images, social networking accounts, phone number, and emailaddresses.

The entity of interest is also referred to as a “seed entity” or simplya “seed”. Each of these properties associated with the seed entities canbe referred to as an “entity property.” As used herein, the term“entity” refers to any real world entity that has attributes useful foridentifying the entity. An entity can be a person or an organization,and can also be an account, a place, or an event. Attributes for theentity include, for example, names, identification number,characteristics and address, without limitation.

During the optional pre-search, entities related with the seed entitymay be uncovered. Such other entities are hereinafter referred to as“related entities” or “seed-linked entities”. The relationship betweenthe seed entity and a seed-linked entity is hereinafter referred to as a“link”. Known links or validated links are referred to as “hard links,”and potential links uncovered during a search (not yet validated) arereferred to as “soft links.”

A seed entity may be linked to one or more seed-linked entities. Forinstance, for a person as a seed entity, a seed-linked entity can be afinancial institution where the person has an account or has conductedtransactions. For the same person, another seed-linked entity may be asecond person that co-owns a shop with the seed person.

In some embodiments, one or more properties of a seed entity are used togenerate a search query. In some embodiments, a search query includes atleast a property of one or more seed-linked entities. In someembodiments, a set of different search queries are generated. Each ofthem may include a property of one of the entities, but the setcollectively represents a combination of different properties ofdifferent entities. As illustrated in FIG. 1, the collection of a seedentity and one more hard-linked seed-linked entities constitutes a “seedcluster.”

The term “database” or “data source” may refer to any data structure forstoring and/or organizing data, including, but not limited to,relational databases (Oracle database, mySQL database, Cassandradatabase, etc.), spreadsheets, XML, files, and text file, among others.In some embodiments, a database schema of a database system is itsstructure described in a formal language supported by the databasemanagement system.

The term “data compression,” as commonly used in signal processing,involves encoding information using fewer bits than the originalrepresentation. Compression can be either lossy or lossless. Losslesscompression reduces bits by identifying and eliminating statisticalredundancy. No information is lost in lossless compression.

The term “Huffman coding” refers to the use of a particular type ofoptimal prefix code used for lossless data compression. The output fromHuffman coding can be viewed as a variable-length code table forencoding a source symbol (such as a character in a file). The algorithmderives this table from the estimated probability or frequency ofoccurrence (weight) for each possible value of the source symbol. As inother entropy encoding methods, more common symbols are generallyrepresented using fewer bits than less common symbols. Huffman's methodcan be efficiently implemented, finding a code in time linear to thenumber of input weights if these weights are sorted.

Compilation and Extension of Search Queries

A plurality of search queries built upon the properties of entities in aseed cluster can be used for one or more rounds of comprehensivesearches. As explained above, the properties of the seed entity andseed-linked entities can be obtained from an optional pre-search, oralternatively retrieved from a pre-existing data source or provided by auser.

The search queries, in some embodiments, not only include ones withdifferent properties of the seed entity, but also those built uponproperties of various seed-linked entities. Given that each of theentities can have multiple properties, and that there may be multipleseed-linked entities (see, e.g., illustration in FIG. 1), acomprehensive set of search queries can be compiled. In someembodiments, at least one of the search queries includes at least oneproperty of the seed entity and another search query includes oneproperty of a seed-linked entity. In some embodiments, at least two ofthe search queries each includes a different property of the seed entityand another search query includes at least a property of a seed-linkedentity. In some embodiments, at least one property of the seed entityand properties of at least two seed-linked entities are included indifferent search queries. In some embodiments, the plurality of searchqueries includes search queries representing different combinations ofproperties of the seed entity and different combinations of the linkedseed entity/seed-linked entity pairs.

Furthermore, in addition to the exact phrases (e.g., names and address)of the properties, different variations of the phrases can also beincluded. Variations of a name, for instance, can include a name'sinitial letter, a nickname, or a different spelling of the name.Variations can also be commonly misspelled words, for example.

Therefore, a large number of search queries can be generated. In someembodiments, these search queries can be prioritized, optimized, orconsolidated. One example approach for optimization is to check andremove some redundancy, or in other words, to select a smaller, diverseand yet representative subset of the search queries. In another example,the search queries are ranked, e.g., according to an estimatedprobability of the search queries returning meaningful or desired hits.The top-ranked search queries can then be selected to form a smaller setof search queries, or alternatively certain lower-ranked search queriesare eliminated. More details of search optimization and prioritizationare provided below.

In some embodiments, after an initial round of searches is conductedwith the search queries, one or more rounds of additional searches canbe run. The additional searches, in some embodiments, can use searchqueries that are optimized or updated which takes advantage of theinitial search results. For instance, in the first round of search, thesearch queries may include ones that include properties of the entityand a related entity (e.g., Joe Smith and Bank of the World). The searchresults then suggest a relationship between Joe Smith and Jane Johnsonthrough transactions carried out at Bank of the World. Soft links areaccordingly created between Joe Smith and Jane Johnson and between Bankof the World and Jane Johnson.

In addition, a validated data source indicates that Jane Johnson ishard-linked to Bank of the Universe. Accordingly, either or both of JaneJohnson and Bank of the Universe can be added to the search queriesduring the next round of searches. As provided earlier, the extendedsearch queries generated with this additional information can includedifferent permutations of the information and the variants thereof.

Search Result Cleanup, Tagging, Aggregation and Ranking

Some results returned from the searches may be well-defined entries in adatabase, such as a record of a financial transaction. The record mayinclude an entry for each party to the transaction, the bank, theaccount numbers, and the amount of the transaction.

When a search is carried out against an unstructured data source, suchas a collection of documents, the search results are less structured.For example, a search result (or “hit”) may be a report that includesthe names of entities and bank account number in plain text with nomarking or identification. For an unstructured search result,potentially relevant words, phrases, or other strings can be tagged ormarked to facilitate further analysis. Automated tagging can be donewith methods including the use of natural language processing analysisand predefined regular expressions.

It is also possible that some of the information in a document is notformatted optimally for processing. For instance, phone numbers mayinclude various hyphens and brackets, first and last names may bearranged differently, and addresses can come in different formats.Accordingly, an optional cleanup step can be carried out, such as byadopting a standardized format for each type of data of interest. Forinstance, for all strings that are recognized as U.S. phone numbers,they can be reformatted as (XXX)-XXX-XXXX if not already in this format.With such cleanup and tagging, each search result can be represented asa potentially matched entity with one or more properties or one or morerelated entities.

Sometimes, two or more search results are likely related as determinedby, for instance, their source or common use of identifying informationor properties of certain entities. In such a scenario, these searchresults can be aggregated to represent a single hit. With or withoutaggregation, a search result can be represented as a “hit cluster” (FIG.1), which includes properties of a hit entity, and properties of one ormore entities believed to be linked to the hit entity (and thus referredto as “hit-linked entities”).

One of the advantages of one embodiment the present technology is theability to provide to an analyst simplified, relevant and useful searchresults for the analyst to further analyze. This is particularly helpfulwhen the amount of search results generated from the searches is large.In one embodiment, identification of relevant and useful search resultsis based on provision of a score for each of the hit clusters. Scoring ahit cluster can be done by taking into consideration one or more of thefollowing factors: (a) likelihood of a match between the seed entity andthe hit entity or between a seed-linked entity and a hit-linked entity(b) presence of a new entity in the search result not present in thesearch queries or a difference between the new entity and an entitypresent in the search queries, and (c) characteristics of the new entityin the search result, e.g., type and time since creation. In otherwords, factor (a) concerns the “validity” of the hit cluster; factor (b)concerns the “novelty” of the hit cluster, i.e., whether a user isalready aware of the information included in the hit cluster, and factor(c) concerns the value of the hit cluster. Each of these factors isdiscussed in further details below.

For the purpose of illustration, an entity, represented by

, is a member of a set of entities, collectively represented by E.Entities can have a set of directed links L⊆EλE, and properties P. Foran entity

∈E, in some embodiments, let

·p⊆P denote the properties associated with entity

. A cluster around entity e can be referred to as c(

)={υ∈E|(

, υ)∈L or (υ,

)∈L). Edge relation L is not necessarily symmetric.

In order to score or rank the hit clusters, in one embodiment, each hitcluster is evaluated for its validity, novelty, and value (three facets)and is given a probability score between [0, 1]. In some embodiments,ranking of the hit clusters is not required, as the probability scorescan be directly used to select top results for further consideration andanalysis by an analyst.

In some embodiments, it is assumed that each facet is independent, andthe probability score of the hit cluster can be obtained asp(valid)*p(valuable)*p(novel). In a preferred embodiment, the scoring ofvaluable and novel is bundled as they can be more closely related, andthus the probability scoring can be obtained as p(valid)*p(valuable,novel). For each of the three facets discussed below, the score can becalculated by a deterministic function of the seed entity s∈E, hit υ,clusters c(s), c(υ), and the queries matching s→υQ={(backend,prop_(seed), prop_(hit), query, c), . . . }. In each query q∈Q backendis the data source where the query was run, prop_(seed) ∈s.p the seedproperty used to generate the query, prop_(hit) ∈v.p the property of thetarget object the search hit on, query the string query run, and c thenumber of search results returned by running query against backend.

I. Validity

For purpose of illustration, a match between an entity e and a hit v isconsidered valid if it's unlikely to have happened spuriously, or bychance. Two example methods are described here for determining whether amatch is non-spurious. One is the “prior”: Given the search string anddata sources, what is the likelihood an exact match on this string wouldspuriously happen? The other is based on the “posterior”: Giveninformation about the corpus, how many search results are returned?These methods can be used alone, in one embodiment, or the results canbe combined, in another embodiment. When the results are combined, inone embodiment, their probability scores can be multiplied, which thenrequires both high probability scores for a query to be consideredhigh-quality.

In one example, the validity prior is calculated. In this example, asearch query and response contains the information backend, prop_(seed),query, with response information prop_(hit),c. In examining the prior,in one embodiment, it is assumed that all the information butprop_(hit),c is given. Based on the property type being searched (e.g.,name, social security number, date of birth) and the backend used forthe search, the system may be able to estimate whether a search resultis relevant. In a simplified embodiment, the backend probability, seedproperty prop_(seed) probability, and the probability estimate based onthe query string are assumed to be independent estimates of thelikelihood a match is relevant. One non-limiting way to aggregate theseprobabilities is with the product:P_(backend)P_(seed-property)P_(query). In some embodiments, it isassumed that P_(backend) and P_(seed-property) are switch parameters(mapping from which—property/backend→float), and only P_(query) needs tobe specified.

In the example of using the prior validity assessment, some techniquesfrom information theory can be used. A deterministic compression methodcan be used to match a seed set of strings U against another V. Forsimplicity, in some embodiments, assuming u∈U either exactly matches v∈Vor doesn't (e.g., partial matches). Assuming these strings weregenerated by random bits and a deterministic function over these bits,in one embodiment, compression can be obtained by reversing thisdeterministic function. In some embodiments, for the compressed sets U′and V′ compressed with function c: (U∪V)→(U′∪V′), the propertyu=v⇔c(u)=c(v) can be derived from the function c being deterministic.

In some embodiments, if the prior knowledge is encoded as adeterministic compression function, and the size of set V is given tomatch against (a parameter to tune), the system can calculate theprobability of a spurious match using the simplified model ofpure-random bitstrings.

For search queries that include names of an entity, some embodiments maydetermine how common the names are. A match of a rare name can beconsidered as more reliable than a match of a common name. Therefore, inone embodiment, a corpus of name frequencies can be obtained from theU.S. Census Bureau or other sources.

The validity posterior is discussed next as another example method forassessing the validity of a match. In accordance with one embodiment ofthe disclosure, after a search is run, the number of hits returnedbecomes known. This number can then be used to calculate a probabilitythat the returned result is spurious.

In one embodiment, the probability calculated by simplifying that allsearch results returned are actually-related matches, or all arespurious (resulting from random unrelated text matching). Similar tocalculation of probability prior, in one embodiment, the calculation ofprobability posterior models the query and data sources as the output ofa deterministic process run on a smaller sequence of random bits.

II. Novelty

As described above, a search query may include a seed entity, a relatedentity that links to the entity through a known or validated link (ahard link), optionally soft links (e.g., links generated throughsearches), and potentially more hard links. A match between a seedcluster and a hit cluster is considered novel, in some embodiments, ifthe hit cluster contains an entity, a property, an entity-linked entity,or a link that isn't similar to any of the seed, its properties, itsrelated entities or links. Let ƒ: (P∪E)×(P∪E)→[0,1] be the similarityfunction defined over properties and entities. Let M be the set ofentities in the match cluster, and S the set of entities/properties inthe seed object and seed-links. Then, in some embodiments, the noveltyscore can be obtained as:

novelty(M,S)=1−min_(m∈M){max_(s∈S)ƒ(s,m)}.

III. Value

The value of a potentially matched search result can be determined, inone embodiment, by the type of result. For instance, in fraud-detection,a prior note from an analyst of likely fraud is a stronger indicatorthan an unsuspicious money transfer. For a member of the hit cluster,the probability of value is given by value_(v.type) for any y∈c(v) inthe hit cluster.

The novelty and value of a hit cluster can be considered together, insome embodiments. With respect to a hit cluster, in some embodiments, itis preferred that at least one member of the cluster is both novel andvaluable. If a hit cluster contains one element that's novel but notvaluable, and another element that's valuable but not novel, the clusteras a whole is likely not interesting to a user.

In one embodiment, the value and novelty collectively are defined as:

max_(y∈c(m)){min_(x∈c(s)∪s.p)(novel(x,y)^(δ))value_(y·type)}

which means “the value+novelty of the most interesting element of thematch cluster”. The exponent parameter of novelty may balance the twoscores.

IV. Combined Relevance Function

In some embodiments, it is considered that a match of a seed cluster toa hit cluster is relevant if the match is valid, novel, and valuable, asillustrated below:

P(relevant)=P(valid)P(novel,valuable)

In one embodiment, these probabilities can be replaced with expressionsfrom above to obtain the following:

${P({relevant})} = {\left( {1 - {\prod\limits_{q \in Q}\left( {1 - {p(q)}} \right)}} \right){\max_{y \in {c{(m)}}}\left\{ {\min_{{x \in {{c{(s)}}\bigcup s}},p}{\left( {{novel}\left( {x,y} \right)}^{\delta} \right){value}_{y,{type}}}} \right\}}}$     where$\mspace{79mu} {{p(q)} = {{{}_{P_{backend}P_{{seed} - {property}}P_{{hit} - {property}}}^{}{}_{}^{1/2^{k}}}\frac{1}{1 + {a\; \beta^{c}}}}}$     and$\mspace{79mu} {k = {\left( {\sum\limits_{t = t_{1{\cdots t}_{k}}}{\min \left( {{\log_{2}\frac{1}{P_{name}(t)}},{\left( {\log_{2}26} \right){{len}(t)}}} \right)}} \right) + {5\left( {l - 1} \right)}}}$

for names, and the custom compression calculations detailed above forentity properties such as social security number, or date of birth.

In this example function, all parts are calculable from the seed, thesearch result, and search properties except ∀backends, p_(backend),∀properties, p_(property), α, β, γ, δ, ∀objecttype, v_(y·type), and thefunction “novel”. In one embodiment, the function “novel” may return 1if for two objects with the same id or otherwise calculate the Jaccardindex over alpha-numeric tokenized strings. All other properties,however, are numbers in certain embodiments.

IV. Search Query and Pre-Hit Cluster Prioritization

There are two main potential bottlenecks of the above approach: thelarge number of parallel searches conducted (which could potentiallyoverwhelm the backend), and the large number of links loaded off allentities of the search results.

One example method of reducing the system burden is to prioritize thesearch queries and select a preferred subset of queries to run. Thecutoff in max-number-search-queries can be based on system constraints.In some embodiments, it is considered that a higher-scoring-query ismore valuable to run than any number of low-scoring queries. In someembodiments, a greedy algorithm is used:

p _(estimated)(q)=P _(backend) P _(seed-property) P_(hit-property)γ^(1/2) ^(k) .

This greedy algorithm selects the highest-scoring query, then the nexthighest, and so forth until the system-determined cap on number ofqueries is maxed out. In some embodiments, a dissimilarity constraint isimposed, such that diverse searches are selected. Assuming for eachquery pair q₁, q₂, there is a query-similarity-function ƒ (q₁,q₂)∈{0,1}, then the greedy algorithm changes slightly, in some embodiments, to:

given Q//full set of queries

Q*=∅//set of queries to execute

while |Q*|<max−queries and |Q−Q*|>0

q=argmax_(q∈Q) {P _(estimated)(q)*(1−max(ƒ(q,q′)|q′∈Q*))}.

A first example choice of the function ƒ that works well isƒ(q₁,q₂)=1(q₁·seedprop=q₂·seedprop), guaranteeing that one seed propertytype doesn't dominate the search.

With respect to the second potential bottleneck, in some embodiments,before loading links off the hit cluster, the matches can be prioritizedand the matches with low priority scores are removed. A naturalprioritization is the relevance function defined above. The relevancefunctional form is thus identical to above:

${P({relevant})} = {\left( {1 - {\prod\limits_{q \in Q}\left( {1 - {p(q)}} \right)}} \right){novel}\mspace{11mu} \left( {s,v} \right)^{\delta}\mspace{11mu} {value}_{v,{type}}}$

except that novelty/value are calculated only over the match itself,since the hit-cluster has not been loaded.

Automated Assistance

The search techniques described here can be automated with no or minimumhuman intervention. Therefore, after taking an initial input or commandfrom a user, the system can readily present a prioritized, optimized,and aggregated set of search results to a user for further analysis. Insome embodiment, the user command is provided on a graphic userinterface, and so is the presentation of the search results.

FIG. 2 illustrates a graphic interface 200 that allows a user to conducta simple search for an entity of interest. Form 201 is configured toreceive a search keyword from the user, and the menu bar 202 allows theuser to select search preferences. For example, as illustrated in FIG.2, the search preference can be for any keyword, for a person, for aninstitution, or can be customized. Below the search form, a panel 203 onthe left shows a list of recent searches for the user's convenience. Apanel 204 shows data sources available for the search. It is noted thatone or more of the data sources can be remote such that the searcheswill be also done remotely, and one or more of the data sources may begenerated or stored locally.

After a user enters a simple search term, FIG. 3 illustrates a portionof results returned to the user. On the interface 300, which can be onthe same terminal as seen for interface 200 in FIG. 2, field 301indicates that the search was done with the keyword “Joe Smith.” Thefirst box 302 below is titled “Joe Smith” which is indicated as aperson. On the bottom right of the box, an indicator “Merged Record”indicates that this record is a pre-compiled and pre-curated record forthis person. Accordingly, a list of properties is provided to theperson, some of which are shown. Properties for a person may be name(and name variants), date of birth, SSN, and without limitation.

By contrast, the information displayed in box 303 is less organized. Itshows a record ID and type, and some information (e.g., date of creationand narrative) relevant to the record or the search query. Some wordsand phrases are marked (by underlying) in box 303. As explained above ina different context, such marking (also referred to as tagging) ishelpful for user analysis.

Box 302 presents a collection of curated, aggregated or validatedinformation for the person Joe Smith. When the user clicks on this box,the user is directed to a new interface 400 in FIG. 4. In addition tothe properties in box 401 which are already shown in FIG. 3, FIG. 4 alsoincludes a box listing data records that are available from differentdata sources. Such records can be understood as “obvious hits” probablybecause the entities identified in these records may have perfectmatches with multiple properties of Joe Smith.

In some embodiments, the box 403, titled “Automated Assistance,”presents a listing of records that are speculated by the system aspotentially relevant to Joe Smith. The method and procedure for theidentification of these records are described in details in the sectionsabove, including search query generation, compilation andprioritization, and search result filtering and ranking. In someembodiments, when the system generates search queries in an automatedfashion, the system takes one or more properties of the seed entity(e.g., Joe Smith) as well as properties of seed-linked properties, allof which can be already present in this merged record or can be obtainedby automated searches.

An important part of the search queries is the links between the seedentity and related other entities. Such links are graphicallyillustrated in box 404, which allows a user to dig in further detailsfor each link or entity or to tune the search queries if desired. Forinstance, in box 404, the entity in the center is the seed entity JoeSmith. The other entities that are linked to the seed entity include,without limitation, persons, bank accounts, phones, email addresses,cases, documents, financial organizations, and locations.

In addition to providing a graphic interface that enables anon-technical user/analyst to explore and analyze search results, thesystem can also be configured to generate alerts based on searchesconducted on the background. When a new search result is identified,panel 405 shows an alert message to the user. Alternatively, an alertemail can be sent to a user that has shown interest in the seed entity(and an interest to receive such alerts).

Computational Methods and Modules

In accordance with certain embodiments of the present disclosure, FIG. 5is provided to illustrate a flowchart of an example method 500 foridentifying information relating to an entity for analysis. The method500 may be implemented in various environments including, for example,the system as described herein. The operations of method 500 presentedbelow are intended to be illustrative. Depending on the implementation,the example method 500 may include additional, fewer, or alternativesteps performed in various orders or in parallel. The example method 500may be implemented in various computing systems or devices including oneor more processors.

At block 502, a computer system conducts an optional pre-search with asearch query comprising a seed entity. At block 504, the systemgenerates a plurality of search queries each comprising a property of aseed entity or an entity associated with the seed entity. In someembodiments, the plurality of search queries can be optimized byeliminating search queries that are relatively less likely to returndesirable search results and/or by reducing redundancy (block 506). Atblock 508, the system conducts searches, with the search queries, toobtain a plurality of search results, wherein each search resultcomprises a hit entity and one or more entities associated with the hitentity.

At block 510, the system determines a score for each of the searchresults, taking as input (a) likelihood of match between the seed entityand the hit entity or between an entity associated with the seed entityand an entity associated with the hit entity, (b) presence of a newentity in the search result not present in the search queries or adifference between the new entity and an entity present in the searchqueries, and (c) characteristics of the new entity in the search result.Optionally, the search results can be ranked based on the scores (block512), and/or the system provides one or more search results based on thescores to a user for analysis (block 514).

Hardware Implementation

The techniques described herein are implemented by one or morespecial-purpose computing devices. The special-purpose computing devicesmay be hard-wired to perform the techniques, or may include circuitry ordigital electronic devices such as one or more application-specificintegrated circuits (ASICs) or field programmable gate arrays (FPGAs)that are persistently programmed to perform the techniques, or mayinclude one or more hardware processors programmed to perform thetechniques pursuant to program instructions in firmware, memory, otherstorage, or a combination. Such special-purpose computing devices mayalso combine custom hard-wired logic, ASICs, or FPGAs with customprogramming to accomplish the techniques. The special-purpose computingdevices may be desktop computer systems, server computer systems,portable computer systems, handheld devices, networking devices or anyother device or combination of devices that incorporate hard-wiredand/or program logic to implement the techniques.

Computing device(s) are generally controlled and coordinated byoperating system software, such as iOS, Android, Chrome OS, Windows XP,Windows Vista, Windows 7, Windows 8, Windows Server, Windows CE, Unix,Linux, SunOS, Solaris, iOS, Blackberry OS, VxWorks, or other compatibleoperating systems. In other embodiments, the computing device may becontrolled by a proprietary operating system. Conventional operatingsystems control and schedule computer processes for execution, performmemory management, provide file system, networking, I/O services, andprovide a user interface functionality, such as a graphical userinterface (“GUI”), among other things.

Also provided is a computer system 600 upon which any of the embodimentsdescribed herein may be implemented. The computer system 600 includes abus 602 or other communication mechanism for communicating information,one or more hardware processors 604 coupled with bus 602 for processinginformation. Hardware processor(s) 604 may be, for example, one or moregeneral purpose microprocessors.

The computer system 600 also includes a main memory 606, such as arandom access memory (RAM), cache and/or other dynamic storage devices,coupled to bus 602 for storing information and instructions to beexecuted by processor 604. Main memory 606 also may be used for storingtemporary variables or other intermediate information during executionof instructions to be executed by processor 604. Such instructions, whenstored in storage media accessible to processor 604, render computersystem 600 into a special-purpose machine that is customized to performthe operations specified in the instructions.

The computer system 600 further includes a read only memory (ROM) 608 orother static storage device coupled to bus 602 for storing staticinformation and instructions for processor 604. A storage device 610,such as a magnetic disk, optical disk, or USB thumb drive (Flash drive),etc., is provided and coupled to bus 602 for storing information andinstructions.

The computer system 600 may be coupled via bus 602 to a display 612,such as a cathode ray tube (CRT) or LCD display (or touch screen), fordisplaying information to a computer user. An input device 614,including alphanumeric and other keys, is coupled to bus 602 forcommunicating information and command selections to processor 604.Another type of user input device is cursor control 616, such as amouse, a trackball, or cursor direction keys for communicating directioninformation and command selections to processor 604 and for controllingcursor movement on display 612. This input device typically has twodegrees of freedom in two axes, a first axis (e.g., x) and a second axis(e.g., y), that allows the device to specify positions in a plane. Insome embodiments, the same direction information and command selectionsas cursor control may be implemented via receiving touches on a touchscreen without a cursor.

The computing system 600 may include a user interface module toimplement a GUI that may be stored in a mass storage device asexecutable software codes that are executed by the computing device(s).This and other modules may include, by way of example, components, suchas software components, object-oriented software components, classcomponents and task components, processes, functions, attributes,procedures, subroutines, segments of program code, drivers, firmware,microcode, circuitry, data, databases, data structures, tables, arrays,and variables.

In general, the word “module,” as used herein, refers to logic embodiedin hardware or firmware, or to a collection of software instructions,possibly having entry and exit points, written in a programminglanguage, such as, for example, Java, C or C++. A software module may becompiled and linked into an executable program, installed in a dynamiclink library, or may be written in an interpreted programming languagesuch as, for example, BASIC, Perl, or Python. It will be appreciatedthat software modules may be callable from other modules or fromthemselves, and/or may be invoked in response to detected events orinterrupts. Software modules configured for execution on computingdevices may be provided on a computer readable medium, such as a compactdisc, digital video disc, flash drive, magnetic disc, or any othertangible medium, or as a digital download (and may be originally storedin a compressed or installable format that requires installation,decompression or decryption prior to execution). Such software code maybe stored, partially or fully, on a memory device of the executingcomputing device, for execution by the computing device. Softwareinstructions may be embedded in firmware, such as an EPROM. It will befurther appreciated that hardware modules may be comprised of connectedlogic units, such as gates and flip-flops, and/or may be comprised ofprogrammable units, such as programmable gate arrays or processors. Themodules or computing device functionality described herein arepreferably implemented as software modules, but may be represented inhardware or firmware. Generally, the modules described herein refer tological modules that may be combined with other modules or divided intosub-modules despite their physical organization or storage.

The computer system 600 may implement the techniques described hereinusing customized hard-wired logic, one or more ASICs or FPGAs, firmwareand/or program logic which in combination with the computer systemcauses or programs computer system 600 to be a special-purpose machine.According to one embodiment, the techniques herein are performed bycomputer system 600 in response to processor(s) 604 executing one ormore sequences of one or more instructions contained in main memory 606.Such instructions may be read into main memory 606 from another storagemedium, such as storage device 610. Execution of the sequences ofinstructions contained in main memory 606 causes processor(s) 604 toperform the process steps described herein. In alternative embodiments,hard-wired circuitry may be used in place of or in combination withsoftware instructions.

The term “non-transitory media,” and similar terms, as used hereinrefers to any media that store data and/or instructions that cause amachine to operate in a specific fashion. Such non-transitory media maycomprise non-volatile media and/or volatile media. Non-volatile mediaincludes, for example, optical or magnetic disks, such as storage device610. Volatile media includes dynamic memory, such as main memory 606.Common forms of non-transitory media include, for example, a floppydisk, a flexible disk, hard disk, solid state drive, magnetic tape, orany other magnetic data storage medium, a CD-ROM, any other optical datastorage medium, any physical medium with patterns of holes, a RAM, aPROM, and EPROM, a FLASH-EPROM, NVRAM, any other memory chip orcartridge, and networked versions of the same.

Non-transitory media is distinct from but may be used in conjunctionwith transmission media. Transmission media participates in transferringinformation between non-transitory media. For example, transmissionmedia includes coaxial cables, copper wire and fiber optics, includingthe wires that comprise bus 602. Transmission media can also take theform of acoustic or light waves, such as those generated duringradio-wave and infra-red data communications.

Various forms of media may be involved in carrying one or more sequencesof one or more instructions to processor 604 for execution. For example,the instructions may initially be carried on a magnetic disk or solidstate drive of a remote computer. The remote computer can load theinstructions into its dynamic memory and send the instructions over atelephone line using a modem. A modem local to computer system 600 canreceive the data on the telephone line and use an infra-red transmitterto convert the data to an infra-red signal. An infra-red detector canreceive the data carried in the infra-red signal and appropriatecircuitry can place the data on bus 602. Bus 602 carries the data tomain memory 606, from which processor 604 retrieves and executes theinstructions. The instructions received by main memory 606 may retrieveand execute the instructions. The instructions received by main memory606 may optionally be stored on storage device 610 either before orafter execution by processor 604.

The computer system 600 also includes a communication interface 618coupled to bus 602. Communication interface 618 provides a two-way datacommunication coupling to one or more network links that are connectedto one or more local networks. For example, communication interface 618may be an integrated services digital network (ISDN) card, cable modem,satellite modem, or a modem to provide a data communication connectionto a corresponding type of telephone line. As another example,communication interface 618 may be a local area network (LAN) card toprovide a data communication connection to a compatible LAN (or WANcomponent to communicated with a WAN). Wireless links may also beimplemented. In any such implementation, communication interface 618sends and receives electrical, electromagnetic or optical signals thatcarry digital data streams representing various types of information.

A network link typically provides data communication through one or morenetworks to other data devices. For example, a network link may providea connection through local network to a host computer or to dataequipment operated by an Internet Service Provider (ISP). The ISP inturn provides data communication services through the world wide packetdata communication network now commonly referred to as the “Internet”.Local network and Internet both use electrical, electromagnetic oroptical signals that carry digital data streams. The signals through thevarious networks and the signals on network link and throughcommunication interface 618, which carry the digital data to and fromcomputer system 600, are example forms of transmission media.

The computer system 600 can send messages and receive data, includingprogram code, through the network(s), network link and communicationinterface 618. In the Internet example, a server might transmit arequested code for an application program through the Internet, the ISP,the local network and the communication interface 618.

The received code may be executed by processor 604 as it is received,and/or stored in storage device 610, or other non-volatile storage forlater execution.

Each of the processes, methods, and algorithms described in thepreceding sections may be embodied in, and fully or partially automatedby, code modules executed by one or more computer systems or computerprocessors comprising computer hardware. The processes and algorithmsmay be implemented partially or wholly in application-specificcircuitry.

The various features and processes described above may be usedindependently of one another, or may be combined in various ways. Allpossible combinations and sub-combinations are intended to fall withinthe scope of this disclosure. In addition, certain method or processblocks may be omitted in some implementations. The methods and processesdescribed herein are also not limited to any particular sequence, andthe blocks or states relating thereto can be performed in othersequences that are appropriate. For example, described blocks or statesmay be performed in an order other than that specifically disclosed, ormultiple blocks or states may be combined in a single block or state.The example blocks or states may be performed in serial, in parallel, orin some other manner. Blocks or states may be added to or removed fromthe disclosed example embodiments. The example systems and componentsdescribed herein may be configured differently than described. Forexample, elements may be added to, removed from, or rearranged comparedto the disclosed example embodiments.

Conditional language, such as, among others, “can,” “could,” “might,” or“may,” unless specifically stated otherwise, or otherwise understoodwithin the context as used, is generally intended to convey that certainembodiments include, while other embodiments do not include, certainfeatures, elements and/or steps. Thus, such conditional language is notgenerally intended to imply that features, elements and/or steps are inany way required for one or more embodiments or that one or moreembodiments necessarily include logic for deciding, with or without userinput or prompting, whether these features, elements and/or steps areincluded or are to be performed in any particular embodiment.

Any process descriptions, elements, or blocks in the flow diagramsdescribed herein and/or depicted in the attached figures should beunderstood as potentially representing modules, segments, or portions ofcode which include one or more executable instructions for implementingspecific logical functions or steps in the process. Alternateimplementations are included within the scope of the embodimentsdescribed herein in which elements or functions may be deleted, executedout of order from that shown or discussed, including substantiallyconcurrently or in reverse order, depending on the functionalityinvolved, as would be understood by those skilled in the art.

It should be emphasized that many variations and modifications may bemade to the above-described embodiments, the elements of which are to beunderstood as being among other acceptable examples. All suchmodifications and variations are intended to be included herein withinthe scope of this disclosure. The foregoing description details certainembodiments of the invention. It will be appreciated, however, that nomatter how detailed the foregoing appears in text, the invention can bepracticed in many ways. As is also stated above, it should be noted thatthe use of particular terminology when describing certain features oraspects of the invention should not be taken to imply that theterminology is being re-defined herein to be restricted to including anyspecific characteristics of the features or aspects of the inventionwith which that terminology is associated. The scope of the inventionshould therefore be construed in accordance with the appended claims andany equivalents thereof.

1. A system for identifying relevant information for an entitycomprising: one or more processors and a memory storing instructionsthat, when executed by the one or more processors, cause the system to:generate a plurality of search queries comprising a seed entity and oneor more entities associated with the seed entity; conduct searches, withthe search queries, in one or more data sources to obtain a plurality ofsearch results, wherein each search result comprises a hit entity andone or more entities associated with the hit entity; and determine ascore for each of the search results, taking as input (a) likelihood ofmatch between the seed entity and the hit entity or between an entityassociated with the seed entity and an entity associated with the hitentity, (b) presence of a new entity in the search result not present inthe search queries or a difference between the new entity and an entitypresent in the search queries, and (c) characteristic of the new entityin the search result.
 2. The system of claim 1, wherein the instructionsfurther cause the system to provide one or more search results based onthe scores to a user for analysis.
 3. The system of claim 1, wherein atleast one of the search queries comprises a third entity associated withone of the entities associated with the seed entity, wherein the thirdentity is at least not known as directly associated with the seedentity.
 4. The system of claim 3, wherein the third entity is identifiedfrom a pre-search with a search query that comprises the seed entity andthe one or more entities associated with the seed entity.
 5. The systemof claim 1, wherein the seed entity or the one or more entitiesassociated with the seed entity is represented by a property of therespective entity, wherein the property is selected from the groupconsisting of name, address, date of birth, social security number, cityof birth, image, social networking account, phone number and emailaddress.
 6. The system of claim 1, wherein the instructions furthercause the system to eliminate search queries less likely to returndesired search results.
 7. The system of claim 1, wherein determinationof likelihood of match comprises the use of a data compression method todetermine a likelihood that the entities match with each other bychance.
 8. The system of claim 7, wherein the data compression methodcomprises the use of Huffman coding.
 9. The system of claim 1, whereinwhen an entity is represented by a person's name, the determination oflikelihood of match comprises determination of frequency of use of thename.
 10. The system of claim 1, wherein the characteristic of theentity is compared to a predefined list of characteristics of entitiesto determine the value of the characteristic.
 11. A computer-implementedmethod comprising: generating, on a suitably programmed computingdevice, a plurality of search queries comprising a seed entity and oneor more entities associated with the seed entity; conducting searches,with the search queries, in one or more data sources to obtain aplurality of search results, wherein each search result comprises a hitentity and one or more entities associated with the hit entity; anddetermining a score for each of the search results, taking as input (a)likelihood of match between the seed entity and the hit entity orbetween an entity associated with the seed entity and an entityassociated with the hit entity, (b) presence of a new entity in thesearch result not present in the search queries or a difference betweenthe new entity and an entity present in the search queries, and (c)characteristic of the new entity in the search result.
 12. The method ofclaim 1, further comprising providing one or more search results basedon the scores to a user for analysis.
 13. The method of claim 11,wherein at least one of the search queries comprises a third entityassociated with one of the entities associated with the seed entity,wherein the third entity is at least not known as directly associatedwith the seed entity.
 14. The method of claim 11, wherein the seedentity or the one or more entities associated with the seed entity isrepresented by a property of the respective entity, wherein the propertyis selected from the group consisting of name, address, date of birth,social security number, city of birth, image, social networking account,phone number and email address.
 15. The method of claim 11, furthercomprising eliminating search queries less likely to return desiredsearch results.
 16. The method of claim 11, wherein determination oflikelihood of match comprises the use of a data compression method todetermine a likelihood that the entities match with each other bychance.
 17. The method of claim 16, wherein the data compression methodcomprises the use of Huffman coding.
 18. The method of claim 11, whereinwhen an entity is represented by a person's name, the determination oflikelihood of match comprises determination of frequency of use of thename.
 19. The method of claim 11, wherein the characteristic of theentity is compared to a predefined list of characteristics of entitiesto determine the value of the characteristic.
 20. A non-transitorycomputer readable medium comprising instructions that, when executed,cause one or more processors to perform: generating, on a suitablyprogrammed computing device, a plurality of search queries comprising aseed entity and one or more entities associated with the seed entity;conducting searches, with the search queries, in one or more datasources to obtain a plurality of search results, wherein each searchresult comprises a hit entity and one or more entities associated withthe hit entity; and determining a score for each of the search results,taking as input (a) likelihood of match between the seed entity and thehit entity or between an entity associated with the seed entity and anentity associated with the hit entity, (b) presence of a new entity inthe search result not present in the search queries or a differencebetween the new entity and an entity present in the search queries, and(c) characteristic of the new entity in the search result.